10 Things I Wish Someone Told Me Before Starting My MS In Applied Analytics

I started my Masters in Applied Analytics at Columbia University last August. At the time I was totally unaware of what I was getting my self into. I am starting my final semester and I thought it would be useful to reflect on what I wish people had told me. Here are my top ten things:

  1. Hey create a GitHub profile!

One of the best places to showcase your work is GitHub. There are numerous articles out there to explain the benefits, so I won’t go into detail here. However, the best reasons for students are:

  • It is a place to direct hiring managers and recruiters to be able to see your portfolio of projects.
  • It will be a great resource to help project coordination and version control.
  • It is hosts projects and challenges that you can collaborate on.
  1. Try using R-markdown or Jupyter Notebook.

When considering how to share your work it is not convenient to copy paste code or even print screen or snip sections. The standard way is to compile your work into a readable format such as a PDF, Word, or HTML document. Comments are also key to understanding code. Sometimes detailed setup and explanations are needed as well. R-markdown is the R package that allows you to do this in R and Jupyter Notebook in Python. This will be very useful when creating and sharing your GitHub projects.

  1. Make sure to build or update Linked-in and social media.

You will be networking with students, professors, lecturers, guest speakers and experts in the field. Be prepared to share your information and keep it updated. The best place to find a job is through your network so keep them informed of progress through social media.

  1. You need to start networking.

Building on the previous point, try to grow your network. Meet your fellow students and find out what they are doing. Help others through challenges they might be facing and crowd source your questions to get help when needed. Seek counsel and mentors in the faculty as that is what they are there for. Find those professionals that inspire and motivate you in your industry. Go to all networking events and schedule time each week to review your network and keep those connections alive. You shouldn’t however call or ask questions every week to everyone in your network. You need to be genuine in your approach and take time to care for those relationships.

  1. Find time to compete in an outside project…as many as you can.

There projects and challenges happening everywhere, on and off campus. Most are open to the public and others are only for graduate students. Use these projects to practice the skills you are learning. These projects will also be a window into what the real-world tasks might require as a data scientist. You will identify your strengths and weaknesses. This is valuable information for you in structuring your course electives and future professional growth.

  1. Find or pick your industry you want to be in.

Try to narrow your projects to focus on the industry you are aiming to get into when you graduate. Having a portfolio is great but having a portfolio that covers common problems in your industry and demonstrates the specific skills those hiring managers are looking for is even better.

  1. Spend time and identify the techniques and tools you want/need to learn.

Depending on your industry you might need skills in text analysis, Bayesian inference, Machine Learning or building AI models. Data science skill all complement each other, but most jobs have a specific list of skills they are looking for. You have a limited amount of time at the university and you need to create your toolbox to compliment the job you want. Certain electives are offered to teach you these skills and they might only be offered once a year so take careful notes when planning your course. Other skills will need to be obtained through online or outside resources. Make time in your schedule to build those needed skills.

  1. Identify all available resources to learn those hard skills and e-text resources to collect for future growth.

Most universities have vast resources available to students. Some have free access to online learning websites that offer certifications. Libraries usually have free licenses to whole e libraries that are available for download. So, find those text books that cover the skills and knowledge needed. Other private resources are offered to students for free so find what is out there and always ask about student discounts or access.

  1. Identify available software and license you access to.

Like the point above software can be very expensive. In data analytics specifically access to cloud computing resources can be very expensive. Universities usually have access to discounted licenses or trial periods for high valued software. Some industries want you to have experience with certain programs such as SAS, CPLEX, DSX, AMPL, Gurobi, …etc. Microsoft has certain software such as Visio and Project that are very useful and are great resources depending on your industry.

  1. Start certification courses and online boot-camps to compliment your courses.

It is one thing to find these resources and collect them, but if you do not use them then you are wasting your time. Enroll in these boot-camps or online courses. Use the software or licenses on projects. Learn and keep learning. Faculty are great, but it is impossible for the curriculum to include everything that you need to know. The field is changing so fast that you need to follow the trends and immerse yourself into this world.

Final point- listen to podcasts or follow the experts. Paying attention to the hottest topics and applications of tools is key in this world. Listening to podcasts can be a window into the experts and what struggles are felt day to day. Understanding the terminology even if you don’t understand the technical aspects is very important to exist in the world of data science. It is so important to know what you don’t know.

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